From 58c662e9dc55452c305d171095345977120378f3 Mon Sep 17 00:00:00 2001
From: Dhruv Dhayal <137479629+BlockNotes-4515@users.noreply.github.com>
Date: Tue, 25 Jun 2024 14:34:56 +0530
Subject: [PATCH] =?UTF-8?q?DAY-1=20OF=5FSUMMER=5FTRAINING=5F(AI/ML)=20=20?=
 =?UTF-8?q?=20=20=20(DAY-1)=20=20=20=F0=9F=8C=9E?=
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dAY-1 OF SUMMER_TRAINING_AI/ML ON DATE : 24/JUNE/2024

NOTE : All Time Period is According to the Indian Standard Time (IST)
---
 Day1_AI_ML_Vaseem (DAY_1_AI_GUIDE).ipynb | 1001 ++++++++++++++++++
 Day1_DhruvDhayal_AI.ipynb                | 1202 ++++++++++++++++++++++
 Day1_dhruvdhayal_ai.py                   |  403 ++++++++
 3 files changed, 2606 insertions(+)
 create mode 100644 Day1_AI_ML_Vaseem (DAY_1_AI_GUIDE).ipynb
 create mode 100644 Day1_DhruvDhayal_AI.ipynb
 create mode 100644 Day1_dhruvdhayal_ai.py

diff --git a/Day1_AI_ML_Vaseem (DAY_1_AI_GUIDE).ipynb b/Day1_AI_ML_Vaseem (DAY_1_AI_GUIDE).ipynb
new file mode 100644
index 0000000..71f469a
--- /dev/null
+++ b/Day1_AI_ML_Vaseem (DAY_1_AI_GUIDE).ipynb	
@@ -0,0 +1,1001 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "cgOR6WrTmClr",
+        "outputId": "e565404c-59e0-43a7-86cb-020280cfb13e"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "value of A is 10 type: <class 'int'>\n",
+            "value of B is 20.5 type: <class 'float'>\n",
+            "value of C is Hello type: <class 'str'>\n"
+          ]
+        }
+      ],
+      "source": [
+        "A = 10\n",
+        "B = 20.5\n",
+        "C = \"Hello\"\n",
+        "\n",
+        "print(\"value of A is\",A,\"type:\",type(A))\n",
+        "print(\"value of B is\",B,\"type:\",type(B))\n",
+        "print(\"value of C is\",C,\"type:\",type(C))"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "_1A=20"
+      ],
+      "metadata": {
+        "id": "nyv3VOBIoQ30"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "explainations or the understanding of the\n",
+        "code"
+      ],
+      "metadata": {
+        "id": "IQbMFMy2oXCD"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "A = 20\n",
+        "B = 30\n",
+        "\n",
+        "C = A+B# arithmetic operators (+,-,/,*)\n",
+        "print(C)\n",
+        "print(\"the addition of \",A,\"and\",B,'is',C)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "pyemf1seob3j",
+        "outputId": "cd7ca032-5bba-425d-dca5-f01f0bfd7839"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "50\n",
+            "the addition of  20 and 30 is 50\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "name = input(\"what is your name? \")\n",
+        "print(\"hello \",name,\"Good Morning\")\n",
+        "print(type(name))"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "pnX31_cNqmV6",
+        "outputId": "5756fb24-c68e-45d5-dc59-a96bc287939d"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "what is your name? 243\n",
+            "hello  243 Good Morning\n",
+            "<class 'str'>\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "A = int(input(\"number1: \"))\n",
+        "B = int(input(\"number2: \"))\n",
+        "\n",
+        "C = A+B# arithmetic operators (+,-,/,*)\n",
+        "print(C)\n",
+        "print(\"the addition of \",A,\"and\",B,'is',C)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "2ueRf5ZBrPHp",
+        "outputId": "6a221ce4-da0f-434e-db9a-78b1cd5110c8"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "number1: 100\n",
+            "number2: 200\n",
+            "300\n",
+            "the addition of  100 and 200 is 300\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "A = 21\n",
+        "if(A==20):\n",
+        "  print(\"Welcome to Python coding\")\n",
+        "  print(\"End of the code\")\n",
+        "else:\n",
+        "  print(\"bye python\")\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "Cn35lVuQrxrT",
+        "outputId": "a512b84b-6700-4683-9c20-89ba3449e40a"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "bye python\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from ast import While\n",
+        "A= 20\n",
+        "while(A==21):\n",
+        "  print(\"Welcome to Python coding\")\n",
+        "  print(\"End of the code\")"
+      ],
+      "metadata": {
+        "id": "zCETrG94tgEq"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# write a program to perform arithmetic operations\n",
+        "# (+,-,/,*) on the two number entered by user\n",
+        "# and the operator is also entered by user\n",
+        "# (operation to be performed)\n",
+        "\n",
+        "# then , ask user do you want to continue?\n",
+        "# (y or n) if y repeat the process\n",
+        "# if n then exit the code with proper message\n"
+      ],
+      "metadata": {
+        "id": "qmSHPrWhumLK"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "flag=0 # initialise the flag as 0\n",
+        "\n",
+        "while(flag==0):\n",
+        "  A = int(input(\"number1: \"))\n",
+        "  B = int(input(\"number2: \"))\n",
+        "  op = input(\"enter the operation(+,-,*,/):\")\n",
+        "\n",
+        "  if(op == '+'):\n",
+        "    C = A+B# arithmetic operators (+,-,/,*)\n",
+        "    print('result: ',C)\n",
+        "  elif(op == '-'):\n",
+        "    C = A-B\n",
+        "    print('result: ',C)\n",
+        "  elif(op == '*'):\n",
+        "    C = A*B\n",
+        "    print('result: ',C)\n",
+        "  elif(op == '/'):\n",
+        "    C = A/B\n",
+        "    print('result: ',C)\n",
+        "  else:\n",
+        "    print(\"Wrong operation please select (+,-,/,*)\")\n",
+        "  chk = input(\"Do you want to continue (y or n)?\")\n",
+        "  if(chk=='n' or chk == 'N'):\n",
+        "    flag=1# change the flag status\n",
+        "    print(\"program exited\")\n",
+        "  elif(chk=='y' or chk == 'Y'):\n",
+        "    flag=0\n",
+        "    print(\"program continued\")\n",
+        "\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "_9zy2NzXxcYb",
+        "outputId": "74d9cbfc-efd8-4334-d18e-81033a853ea5"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "number1: 30\n",
+            "number2: 40\n",
+            "enter the operation(+,-,*,/):*\n",
+            "result:  1200\n",
+            "Do you want to continue (y or n)?Y\n",
+            "program continued\n",
+            "number1: 12\n",
+            "number2: 21\n",
+            "enter the operation(+,-,*,/):/\n",
+            "result:  0.5714285714285714\n",
+            "Do you want to continue (y or n)?n\n",
+            "program exited\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#functions\n",
+        "# input collection function\n",
+        "\n",
+        "def input_op():\n",
+        "  A = int(input(\"number1: \"))\n",
+        "  B = int(input(\"number2: \"))\n",
+        "  f=0\n",
+        "  while(f==0):\n",
+        "    op = input(\"enter the operation(+,-,*,/):\")\n",
+        "    if(op=='+' or op=='-' or op=='*' or op=='/'):\n",
+        "      f=1\n",
+        "      print('right operator selected')\n",
+        "    else:\n",
+        "      f=0\n",
+        "      print(\"select correct operator\")\n",
+        "  return([A,B,op])\n"
+      ],
+      "metadata": {
+        "id": "YinfB2AwyFdq"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# operation execution\n",
+        "def execution_func(input_list):\n",
+        "  A = input_list[0] # input 1\n",
+        "  B = input_list[1] # input 2\n",
+        "  op = input_list[2] # operation\n",
+        "  if(op == '+'):\n",
+        "    C = A+B# arithmetic operators (+,-,/,*)\n",
+        "    print('result: ',C)\n",
+        "  elif(op == '-'):\n",
+        "    C = A-B\n",
+        "    print('result: ',C)\n",
+        "  elif(op == '*'):\n",
+        "    C = A*B\n",
+        "    print('result: ',C)\n",
+        "  elif(op == '/'):\n",
+        "    C = A/B\n",
+        "    print('result: ',C)\n",
+        "  return([C,op])\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "AAbzqbYc4hsJ"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# continue checking\n",
+        "\n",
+        "def check_execution():\n",
+        "  f2 = 0\n",
+        "  while(f2==0):\n",
+        "    chk = input(\"Do you want to continue (y or n)?\")\n",
+        "    if(chk=='n' or chk == 'N'):\n",
+        "      flag=1# change the flag status\n",
+        "      f2=1\n",
+        "      print(\"program exited\")\n",
+        "    elif(chk=='y' or chk == 'Y'):\n",
+        "      flag=0\n",
+        "      f2=1\n",
+        "      print(\"program continued\")\n",
+        "    else:\n",
+        "      print(\"Wrong char selected\")\n",
+        "      f2=0\n",
+        "\n",
+        "  return(flag)\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "ZVQuIIbL4jmI"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "check_execution()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "zEUiroSu9rK5",
+        "outputId": "e9ec80f2-6167-4402-dad2-211d09a07d8a"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Do you want to continue (y or n)?X\n",
+            "Wrong char selected\n",
+            "Do you want to continue (y or n)?P\n",
+            "Wrong char selected\n",
+            "Do you want to continue (y or n)?y\n",
+            "program continued\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "0"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 23
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# main program\n",
+        "flag=0\n",
+        "while(flag==0):\n",
+        "  input_Lst = input_op() # call the input function to\n",
+        "  # take inputs and operations\n",
+        "\n",
+        "  execution_func(input_Lst) # call the execution function to\n",
+        "  # perform operation\n",
+        "\n",
+        "  flag=check_execution()# check for the continuation\n",
+        "\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "HiVHhyWM9tSI",
+        "outputId": "0429529a-ab4b-4a87-f0b0-1cd8034162cb"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "number1: 34\n",
+            "number2: 34\n",
+            "enter the operation(+,-,*,/):+\n",
+            "right operator selected\n",
+            "result:  68\n",
+            "Do you want to continue (y or n)?n\n",
+            "program exited\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# import third party library in python instance\n",
+        "# command to install the third party library\n",
+        "# pip install name_of_the_module\n",
+        "!pip install numpy\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "JfjPiFnIS6FJ",
+        "outputId": "0cb291a6-8c8a-4bea-fea6-4b17d2f39ff2"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (1.25.2)\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import numpy as np\n",
+        "\n",
+        "# create a numpy array\n",
+        "V = np.array([20,30,40,5])\n",
+        "D = [20,30,40,50]\n",
+        "print(V, type(V))\n",
+        "print(D, type(D))"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "sXQaxPu6TSD_",
+        "outputId": "62a49b2c-cf38-444c-9793-373fdce28ad3"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "[20 30 40  5] <class 'numpy.ndarray'>\n",
+            "[20, 30, 40, 50] <class 'list'>\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# convert the list D into the numpy array\n",
+        "var = np.array(D)\n",
+        "print(var)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "7SrpUAyAT9O-",
+        "outputId": "ef193f52-ae04-4c18-dc9d-f02475688f98"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "[20 30 40 50]\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "var2 = var.copy() # copy the content of the var into var2\n",
+        "print(var)\n",
+        "print(var2)\n",
+        "\n",
+        "R = var+var2\n",
+        "Y = var-var2\n",
+        "Z = var*var2\n",
+        "T = var/var2\n",
+        "print(R)\n",
+        "print(Y)\n",
+        "print(Z)\n",
+        "print(T)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "E1wz_ItlUSaO",
+        "outputId": "21bf7a1e-46e5-4620-827c-655f9640c526"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "[20 30 40 50]\n",
+            "[20 30 40 50]\n",
+            "[ 40  60  80 100]\n",
+            "[0 0 0 0]\n",
+            "[ 400  900 1600 2500]\n",
+            "[1. 1. 1. 1.]\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# few functions\n",
+        "val = np.arange(10) # (start(0),stop(N),step(1))\n",
+        "print(val)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "XwJD02ChUbbe",
+        "outputId": "d5e16332-03dc-44ec-b5ee-8932cdb4c305"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "[0 1 2 3 4 5 6 7 8 9]\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# few functions\n",
+        "val = np.arange(1,10,0.5) # (start(0),stop(N),step(1))\n",
+        "print(val)\n",
+        "print(\"total number of elements \",val.size)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "Aut6KjR5Vk-e",
+        "outputId": "987e450d-34fc-4a5f-a24f-db186400069b"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "[1.  1.5 2.  2.5 3.  3.5 4.  4.5 5.  5.5 6.  6.5 7.  7.5 8.  8.5 9.  9.5]\n",
+            "total number of elements  18\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# linspace\n",
+        "val2 = np.linspace(1,5,200) # (start,stop,num_of_samples)\n",
+        "print('samples number:',val2.size)\n",
+        "print(val2)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "qeehGsAkV4M0",
+        "outputId": "e774d216-277f-432c-ffae-c4a53b6f9410"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "samples number: 200\n",
+            "[1.         1.0201005  1.04020101 1.06030151 1.08040201 1.10050251\n",
+            " 1.12060302 1.14070352 1.16080402 1.18090452 1.20100503 1.22110553\n",
+            " 1.24120603 1.26130653 1.28140704 1.30150754 1.32160804 1.34170854\n",
+            " 1.36180905 1.38190955 1.40201005 1.42211055 1.44221106 1.46231156\n",
+            " 1.48241206 1.50251256 1.52261307 1.54271357 1.56281407 1.58291457\n",
+            " 1.60301508 1.62311558 1.64321608 1.66331658 1.68341709 1.70351759\n",
+            " 1.72361809 1.74371859 1.7638191  1.7839196  1.8040201  1.8241206\n",
+            " 1.84422111 1.86432161 1.88442211 1.90452261 1.92462312 1.94472362\n",
+            " 1.96482412 1.98492462 2.00502513 2.02512563 2.04522613 2.06532663\n",
+            " 2.08542714 2.10552764 2.12562814 2.14572864 2.16582915 2.18592965\n",
+            " 2.20603015 2.22613065 2.24623116 2.26633166 2.28643216 2.30653266\n",
+            " 2.32663317 2.34673367 2.36683417 2.38693467 2.40703518 2.42713568\n",
+            " 2.44723618 2.46733668 2.48743719 2.50753769 2.52763819 2.54773869\n",
+            " 2.5678392  2.5879397  2.6080402  2.6281407  2.64824121 2.66834171\n",
+            " 2.68844221 2.70854271 2.72864322 2.74874372 2.76884422 2.78894472\n",
+            " 2.80904523 2.82914573 2.84924623 2.86934673 2.88944724 2.90954774\n",
+            " 2.92964824 2.94974874 2.96984925 2.98994975 3.01005025 3.03015075\n",
+            " 3.05025126 3.07035176 3.09045226 3.11055276 3.13065327 3.15075377\n",
+            " 3.17085427 3.19095477 3.21105528 3.23115578 3.25125628 3.27135678\n",
+            " 3.29145729 3.31155779 3.33165829 3.35175879 3.3718593  3.3919598\n",
+            " 3.4120603  3.4321608  3.45226131 3.47236181 3.49246231 3.51256281\n",
+            " 3.53266332 3.55276382 3.57286432 3.59296482 3.61306533 3.63316583\n",
+            " 3.65326633 3.67336683 3.69346734 3.71356784 3.73366834 3.75376884\n",
+            " 3.77386935 3.79396985 3.81407035 3.83417085 3.85427136 3.87437186\n",
+            " 3.89447236 3.91457286 3.93467337 3.95477387 3.97487437 3.99497487\n",
+            " 4.01507538 4.03517588 4.05527638 4.07537688 4.09547739 4.11557789\n",
+            " 4.13567839 4.15577889 4.1758794  4.1959799  4.2160804  4.2361809\n",
+            " 4.25628141 4.27638191 4.29648241 4.31658291 4.33668342 4.35678392\n",
+            " 4.37688442 4.39698492 4.41708543 4.43718593 4.45728643 4.47738693\n",
+            " 4.49748744 4.51758794 4.53768844 4.55778894 4.57788945 4.59798995\n",
+            " 4.61809045 4.63819095 4.65829146 4.67839196 4.69849246 4.71859296\n",
+            " 4.73869347 4.75879397 4.77889447 4.79899497 4.81909548 4.83919598\n",
+            " 4.85929648 4.87939698 4.89949749 4.91959799 4.93969849 4.95979899\n",
+            " 4.9798995  5.        ]\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# random value generation\n",
+        "# this function will generate thev random numbers within 0-1 range\n",
+        "np.random.seed(50) # this is the random state variable which should be any\n",
+        "#postive integer\n",
+        "val3 = np.random.random((10))\n",
+        "print(val3)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "ikM3hcm8WwCO",
+        "outputId": "447f9f4c-d95a-4d77-b03b-084dcf6297df"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "[0.49460165 0.2280831  0.25547392 0.39632991 0.3773151  0.99657423\n",
+            " 0.4081972  0.77189399 0.76053669 0.31000935]\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# generate the random numbers in between 10-50\n",
+        "a = 10\n",
+        "b = 50\n",
+        "\n",
+        "val4 = a+(b-a)*np.random.random((20))\n",
+        "print(val4)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "nlxq3YlnXOZd",
+        "outputId": "2ca579ca-ad75-465b-df7a-1f161b401a7c"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "[23.86164818 24.0705927  15.81867426 48.90658739 46.36713752 32.39828415\n",
+            " 22.54362986 45.5280015  36.98292263 25.64349791 30.28736496 30.96414011\n",
+            " 47.12003711 32.85492276 36.73350275 12.09034764 23.08229216 12.25606557\n",
+            " 17.19310776 47.0373269 ]\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# integer random number generation\n",
+        "val5 =  np.random.randint(10,50,size=(50))\n",
+        "print(val5)\n",
+        "print(val5.size)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "1pUz6W66ZDZm",
+        "outputId": "d8b790dc-d609-4c61-ac79-0981de5eb7c4"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "[21 37 37 17 11 18 45 13 31 13 16 25 41 32 10 21 40 43 24 36 32 47 16 40\n",
+            " 46 41 19 10 26 36 14 11 47 23 45 24 12 32 48 10 24 40 36 14 26 39 24 32\n",
+            " 14 21]\n",
+            "50\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# few more numpy functions\n",
+        "\n",
+        "d = np.zeros((10))\n",
+        "print(d)\n",
+        "r = np.ones((10))*2\n",
+        "print(r)\n",
+        "\n",
+        "g = np.full((10),100)\n",
+        "print(g)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "YZxuQeJ4aBU1",
+        "outputId": "0957857c-d5ae-4c92-fe18-9b4bdfe70c6d"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "[0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
+            "[2. 2. 2. 2. 2. 2. 2. 2. 2. 2.]\n",
+            "[100 100 100 100 100 100 100 100 100 100]\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# statistical function\n",
+        "np.random.seed(10)\n",
+        "var2 = np.random.randint(10,100,(50))\n",
+        "\n",
+        "s = var2.sum()\n",
+        "t = var2.min()\n",
+        "m = var2.max()\n",
+        "max_ind = var2.argmax()\n",
+        "min_ind = var2.argmin()\n",
+        "avg = var2.mean()\n",
+        "print(var2)\n",
+        "print()\n",
+        "print(\"min: \",t,'Pos:',min_ind)\n",
+        "print(\"max: \",m,'Pos:',max_ind)\n",
+        "print(\"average: \",avg)\n",
+        "print(\"sum of all the elements: \",s )\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "PkVh7hNJbbGt",
+        "outputId": "9f02fb50-d85f-4b47-e222-028f6db64375"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "[19 25 74 38 99 39 18 83 10 50 46 26 21 64 98 72 43 82 88 59 61 64 87 79\n",
+            " 23 35 23 96 40 40 99 22 75 41 67 46 37 28 87 32 33 21 38 84 98 19 25 28\n",
+            " 90 81]\n",
+            "\n",
+            "min:  10 Pos: 8\n",
+            "max:  99 Pos: 4\n",
+            "average:  53.06\n",
+            "sum of all the elements:  2653\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import matplotlib.pyplot as plt\n",
+        "import numpy as np\n",
+        "# sin function\n",
+        "# cos function\n",
+        "# custom function\n",
+        "X = np.linspace(1,20,100)\n",
+        "Y = np.sin(X)\n",
+        "Z = np.cos(X)\n",
+        "W = 2*np.sin(X)+np.cos(X)\n",
+        "P = np.sin(2*X) - 2*np.cos(X)\n"
+      ],
+      "metadata": {
+        "id": "hfPMJU9AcgH7"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# all functions output in different figure\n",
+        "plt.figure(1,figsize=(5,2))#activting the figure number 1\n",
+        "plt.plot(X,Y,'r',linewidth=4) # 'r','g,'m','c','k','y','b'\n",
+        "plt.title(\"sine function\")\n",
+        "plt.xlabel('Range')\n",
+        "plt.ylabel('sin(X)')\n",
+        "plt.grid('on')\n",
+        "\n",
+        "plt.figure(2,figsize=(5,2))#activting the figure number 1\n",
+        "plt.plot(X,Z,'g',linewidth=4) # 'r','g,'m','c','k','y','b'\n",
+        "plt.title(\"cos function\")\n",
+        "plt.xlabel('Range')\n",
+        "plt.ylabel('cos(X)')\n",
+        "plt.grid('on')\n",
+        "\n",
+        "plt.figure(3,figsize=(5,2))#activting the figure number 1\n",
+        "plt.plot(X,W,'b',linewidth=4) # 'r','g,'m','c','k','y','b'\n",
+        "plt.title(\"custom function\")\n",
+        "plt.xlabel('Range')\n",
+        "plt.ylabel('func(X)')\n",
+        "plt.grid('on')\n",
+        "\n",
+        "plt.figure(4,figsize=(5,2))#activting the figure number 1\n",
+        "plt.plot(X,P,'k',linewidth=4) # 'r','g,'m','c','k','y','b'\n",
+        "plt.title(\"custom function\")\n",
+        "plt.xlabel('Range')\n",
+        "plt.ylabel('func(X)')\n",
+        "plt.grid('on')"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 973
+        },
+        "id": "ZmhAG6UxdoI1",
+        "outputId": "af0bfd87-dbb1-496c-9085-9ed046aadc80"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 500x200 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 500x200 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 500x200 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 500x200 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# comparative analysis\n",
+        "plt.figure(1,figsize=(10,4))#activting the figure number 1\n",
+        "plt.plot(X,Y,'r',linewidth=4,label=\"sin(X)\") # 'r','g,'m','c','k','y','b'\n",
+        "plt.plot(X,Z,'g',linewidth=4,label='Cos(X)')\n",
+        "plt.plot(X,W,'b',linewidth=4,label='func1(X)')\n",
+        "plt.plot(X,P,'m',linewidth=4,label='func2(X)')\n",
+        "plt.title(\"comparative analysis\")\n",
+        "plt.xlabel('Range')\n",
+        "plt.ylabel('function(X)')\n",
+        "plt.grid('on')\n",
+        "plt.legend() # activate the legends"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 358
+        },
+        "id": "LhcL9l1gc1NE",
+        "outputId": "14dd1520-1d42-49c6-f09b-fec659b404b8"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<matplotlib.legend.Legend at 0x7c2dc8aca230>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 85
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x400 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [],
+      "metadata": {
+        "id": "lwcdNLQHg_ck"
+      },
+      "execution_count": null,
+      "outputs": []
+    }
+  ]
+}
\ No newline at end of file
diff --git a/Day1_DhruvDhayal_AI.ipynb b/Day1_DhruvDhayal_AI.ipynb
new file mode 100644
index 0000000..d756ffb
--- /dev/null
+++ b/Day1_DhruvDhayal_AI.ipynb
@@ -0,0 +1,1202 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 0
+        },
+        "id": "UtXpZLy6jdh5",
+        "outputId": "40c37d63-d6f7-4bc7-a4a8-3c0939dd7cb5"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            " --------------- Factorial Program! ---------------\n",
+            "\n",
+            " 1. Enter the Number in order to find it's Factorial: 5\n",
+            "\n",
+            " --> Factorial of  5  is:  720\n"
+          ]
+        }
+      ],
+      "source": [
+        "#Day-1 Of_AI_ML_SUMMER_TRAINING.\n",
+        "print(\"\\n --------------- Factorial Program! ---------------\");\n",
+        "n=int(input(\"\\n 1. Enter the Number in order to find it's Factorial: \"));\n",
+        "fact=1;\n",
+        "for i in range(1,n+1):\n",
+        "  i=i+1;\n",
+        "  fact=fact*i;\n",
+        "print(\"\\n --> Factorial of \",n,\" is: \",fact);\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#Sum of the 'n' Numbers.\n",
+        "print(\"\\n -------------- Sum of the Two or More Numbers! ------------------\");\n",
+        "n=int(input(\"\\n 1. Enter the Number in order to find it's Sum: \"));\n",
+        "sum=0;\n",
+        "for i in range(1,n+1):\n",
+        "  sum=sum+i;\n",
+        "  i=i+1;\n",
+        "  print(\"\\t\", sum);\n",
+        "print(\"\\n --> Sum of \",n,\" is: \",sum);"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 0
+        },
+        "id": "hEhJ_x8kmBQd",
+        "outputId": "c316f59b-f1de-4205-cab7-081e90d8f748"
+      },
+      "execution_count": 6,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            " -------------- Sum of the Two or More Numbers! ------------------\n",
+            "\n",
+            " 1. Enter the Number in order to find it's Sum: 10\n",
+            "\t 1\n",
+            "\t 3\n",
+            "\t 6\n",
+            "\t 10\n",
+            "\t 15\n",
+            "\t 21\n",
+            "\t 28\n",
+            "\t 36\n",
+            "\t 45\n",
+            "\t 55\n",
+            "\n",
+            " --> Sum of  10  is:  55\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#Adding of the Two Numbers.\n",
+        "#1.  Manual Methods.\n",
+        "a=10;\n",
+        "b=90;\n",
+        "c=(a+b);\n",
+        "print(\"\\n Total Sum of \",a,\" and \",b,\" is: \",c);\n",
+        "\n",
+        "#2. User-Input Methods.\n",
+        "a=int(input(\"\\n 1. Enter the First Number: \"));\n",
+        "b=int(input(\"\\n 2. Enter the Second Number: \"));\n",
+        "result=(a+b);\n",
+        "print(f\"\\n\\t--> Total Sum of {a} and {b} is: {result}\");"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 0
+        },
+        "id": "wtqSofIqm3XT",
+        "outputId": "5f5eda8d-27af-487e-efdd-e68a5329fa88"
+      },
+      "execution_count": 10,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            " Total Sum of  10  and  90  is:  100\n",
+            "\n",
+            " 1. Enter the First Number: 123\n",
+            "\n",
+            " 2. Enter the Second Number: 890\n",
+            "\n",
+            "\t--> Total Sum of 123 and 890 is: 1013\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#Using of the While Loop, or never-ending loop.\n",
+        "while(True):\n",
+        "  print(\"Hello World\");  #We, did not print this because it takes lot of memory and space, which may lead to bann of GoogleVColabLisecing."
+      ],
+      "metadata": {
+        "id": "kHtCytFonor_"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#WAP a program to perform Arithmetic Operations (+,-,*,/), on the two no's entered by the user and perform operations on ot!\n",
+        "#(Operations has to been perfored)!\n",
+        "#Then, Ask the User a Question: Do, You Want to Continue?\n",
+        "#If 'n' Exists then its still performing the operations based on the True Valid-Conditions.\n",
+        "flag=True;\n",
+        "while(flag):\n",
+        "  n1=int(input(\"\\n 1. Enter the First Number: \"));\n",
+        "  n2=int(input(\"\\n 2. Enter the Second Number: \"));\n",
+        "  ch=input(\"\\n --> Enter the Operations You want to Perform: \\n 1. Addition(+) \\n2.Substraction(-) \\n3.Multiplication(*) \\n4.Division(/) \\n\\n\");\n",
+        "\n",
+        "  #Checking the Operation in order to performs.\n",
+        "  if(ch=='+'):\n",
+        "    n3=(n1+n2);\n",
+        "    print(\"\\n\\n --> Addition of (\",n1,\"+\",n2,\") is = \",n3);\n",
+        "  elif(ch=='-'):\n",
+        "    n3=(n1-n2);\n",
+        "    print(\"\\n --> Substraction of (\",n1,\"-\",n2,\") is = \",n3);\n",
+        "  elif(ch==''):\n",
+        "     n3=(n1*n2);\n",
+        "     print(\"\\n --> Multiplication of (\",n1,\"*\",n2,\") is = \",n3);\n",
+        "  elif(ch=='/'):\n",
+        "    if(n1>n2):\n",
+        "      n3=(n1/n2);\n",
+        "      print(\"\\n --> Division of (\",n1,\"/\",n2,\") is = \",n3);\n",
+        "    else:\n",
+        "      n3=(n2/n1);\n",
+        "      print(\"\\n --> Division of (\",n2,\"/\",n1,\") is = \",n3);\n",
+        "  else:\n",
+        "    print(\"\\n Invalid Input!\");\n",
+        "\n",
+        "\n",
+        "#Main Driver Source Code Logic Of User-Input\n",
+        "#print(\"\\n ----- Do, You Want to Still continiue Choose (Y/N): \");\n",
+        "  user=input(\"\\n----- Do, You Want to Still continiue Choose (Y/N): \");\n",
+        "  flag=0;\n",
+        "  if(user==\"Y\" or user==\"y\"):\n",
+        "    flag=True;\n",
+        "    print(\"\\n Now, It's Again Executed!\");\n",
+        "    print(\"\\n---------------------------------------\\n\");\n",
+        "  else:\n",
+        "    flag=False;\n",
+        "    print(\"\\n Program Terminated Thank You!\");\n",
+        "    print(\"\\n---------------------------------------\\n\");"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 0
+        },
+        "id": "Z7Cytj2goByb",
+        "outputId": "59244a79-812c-42a1-a6c8-a757a6481a7e"
+      },
+      "execution_count": 15,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            " 1. Enter the First Number: 120\n",
+            "\n",
+            " 2. Enter the Second Number: 78\n",
+            "\n",
+            " --> Enter the Operations You want to Perform: \n",
+            " 1. Addition(+) \n",
+            "2.Substraction(-) \n",
+            "3.Multiplication(*) \n",
+            "4.Division(/) \n",
+            "\n",
+            "+\n",
+            "\n",
+            "\n",
+            " --> Addition of ( 120 + 78 ) is =  198\n",
+            "\n",
+            "----- Do, You Want to Still continiue Choose (Y/N): y\n",
+            "\n",
+            " Now, It's Again Executed!\n",
+            "\n",
+            "---------------------------------------\n",
+            "\n",
+            "\n",
+            " 1. Enter the First Number: 1266\n",
+            "\n",
+            " 2. Enter the Second Number: 24\n",
+            "\n",
+            " --> Enter the Operations You want to Perform: \n",
+            " 1. Addition(+) \n",
+            "2.Substraction(-) \n",
+            "3.Multiplication(*) \n",
+            "4.Division(/) \n",
+            "\n",
+            "/\n",
+            "\n",
+            " --> Division of ( 1266 / 24 ) is =  52.75\n",
+            "\n",
+            "----- Do, You Want to Still continiue Choose (Y/N): n\n",
+            "\n",
+            " Program Terminated Thank You!\n",
+            "\n",
+            "---------------------------------------\n",
+            "\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#Performing the Same Logic Program with the Functions.\n",
+        "#Starting Phase!\n",
+        "\n",
+        "def values():\n",
+        "  no1=int(input(\"\\n 1. Enter the First Number: \"));\n",
+        "  no2=int(input(\"\\n 2. Enter the Second Number: \"));\n",
+        "  ch=input(\"\\n --> Enter the Operations You want to Perform: \\n 1. Addition(+) \\n2.Substraction(-) \\n3.Multiplication(*) \\n4.Division(/) \\n\\n\");\n",
+        "  return no1,no2,ch;\n",
+        "\n",
+        "def operations(no1,no2,ch):\n",
+        "  #Middle-Phase Performing Operations.\n",
+        "  if(ch=='+'):\n",
+        "    n3=(no1+no2);\n",
+        "    print(\"\\n\\n --> Addition of (\",no1,\"+\",no2,\") is = \",n3);\n",
+        "  elif(ch=='-'):\n",
+        "    n3=(no1-no2);\n",
+        "    print(\"\\n --> Substraction of (\",no1,\"-\",no2,\") is = \",n3);\n",
+        "  elif(ch=='*'):\n",
+        "    n3=(no1*no2);\n",
+        "    print(\"\\n --> Multiplication of (\",no1,\"*\",no2,\") is = \",n3);\n",
+        "  elif(ch=='/'):\n",
+        "    if(no1>no2):\n",
+        "      n3=(no1/no2);\n",
+        "      print(\"\\n --> Division of (\",no1,\"/\",no2,\") is = \",n3);\n",
+        "    else:\n",
+        "      n3=(no2/no1);\n",
+        "      print(\"\\n --> Division of (\",no2,\"/\",no1,\") is = \",n3);\n",
+        "  else:\n",
+        "    print(\"\\n Invalid Input!\");\n",
+        "\n",
+        "def checking():\n",
+        "  #Main-Driver Source Code.\n",
+        "  user=input(\"\\n --> Do, You Still Want to Continue? (Y/N): \");\n",
+        "  if(user==\"Y\" or user==\"y\"):\n",
+        "    return True;\n",
+        "    print(\"\\n Now, It's Again Executed!\");\n",
+        "    print(\"\\n---------------------------------------\\n\");\n",
+        "  else:\n",
+        "    return False;\n",
+        "    print(\"\\n Program Terminated!\");\n",
+        "    print(\"\\n---------------------------------------\\n\");\n",
+        "\n",
+        "#Main Calling the Values of the Functions!\n",
+        "flag=True;\n",
+        "while(flag):\n",
+        "  no1,no2,ch=values();\n",
+        "  operations(no1,no2,ch);\n",
+        "  flag=checking();"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 0
+        },
+        "id": "frwPjV0gtAXI",
+        "outputId": "18acbd34-e6d4-43fb-b761-78b7a99ebdfb"
+      },
+      "execution_count": 17,
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "\n",
+            " 1. Enter the First Number: 120\n",
+            "\n",
+            " 2. Enter the Second Number: 90\n",
+            "\n",
+            " --> Enter the Operations You want to Perform: \n",
+            " 1. Addition(+) \n",
+            "2.Substraction(-) \n",
+            "3.Multiplication(*) \n",
+            "4.Division(/) \n",
+            "\n",
+            "/\n",
+            "\n",
+            " --> Division of ( 120 / 90 ) is =  1.3333333333333333\n",
+            "\n",
+            " --> Do, You Still Want to Continue? (Y/N): y\n",
+            "\n",
+            " 1. Enter the First Number: 89\n",
+            "\n",
+            " 2. Enter the Second Number: 6\n",
+            "\n",
+            " --> Enter the Operations You want to Perform: \n",
+            " 1. Addition(+) \n",
+            "2.Substraction(-) \n",
+            "3.Multiplication(*) \n",
+            "4.Division(/) \n",
+            "\n",
+            "+\n",
+            "\n",
+            "\n",
+            " --> Addition of ( 89 + 6 ) is =  95\n",
+            "\n",
+            " --> Do, You Still Want to Continue? (Y/N): n\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#Import third party Library in Python Instance.\n",
+        "#Command to install third_party_Library.\n",
+        "#Pip Install Name of the Three Modules.\n",
+        "\n",
+        "!pip install numpy;"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 0
+        },
+        "id": "WmHciBBaxJem",
+        "outputId": "72e65a45-1fbb-4ccd-e79b-4c64b493baf8"
+      },
+      "execution_count": 18,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (1.25.2)\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "<!  Starting of the Numpy Library and its's Functions & Operations.>"
+      ],
+      "metadata": {
+        "id": "msP6PWlczYdi"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#Now, Importing the Numpy & then perform all functions and it's operatiosns.\n",
+        "\n",
+        "import numpy as np\n",
+        "V=np.array([1,2,3,4,5]);\n",
+        "D=[1,2,3,4,5];\n",
+        "print(\"\\n Values of \",V,\" and type of item it Contains: \",type(V));\n",
+        "print(\"\\n Values of \",D,\" and type of item it Contains: \",type(D));"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 0
+        },
+        "id": "4FjA7WmizMRc",
+        "outputId": "55366237-59db-4ff1-f515-5a0cb6bd8984"
+      },
+      "execution_count": 19,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            " Values of  [1 2 3 4 5]  and type of item it Contains:  <class 'numpy.ndarray'>\n",
+            "\n",
+            " Values of  [1, 2, 3, 4, 5]  and type of item it Contains:  <class 'list'>\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#checking the Dimensions of the Array.\n",
+        "#NOTE: In, Python Numpy we used to call Dimensions as an axes!\n",
+        "import numpy as np;\n",
+        "check=np.array([[1,2] , [3,4] , [5,6],  [7,8]]);\n",
+        "print(\"\\n Dimensions of the Array: \",check.ndim);"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 0
+        },
+        "id": "Hkc4zvx9z-wl",
+        "outputId": "b81a3b7e-cbb4-4f59-af2f-044bc92cdbe4"
+      },
+      "execution_count": 20,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            " Dimensions of the Array:  2\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#Calculating the Total Size of an Array!\n",
+        "#NOTE: Both, shape() & size() is similar to find the total length of item in any dimension of numpy array!\n",
+        "import numpy as np;\n",
+        "checking=np.array([[[1,2,3,4], [4,5,6,7], [6,7,8,9], [3,44,56,100]]]);\n",
+        "\n",
+        "#Checking the Dimensions of the Matrix.\n",
+        "print(\"\\n 1. Dimension of the Matrix is: \",checking.ndim);\n",
+        "print(\"\\n 3. Size of the Matrix are: \",checking.size);\n",
+        "print(\"\\n 2. Total Axes of the Matrix are: \",checking.shape);"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 0
+        },
+        "id": "DcvXK1jv0eHA",
+        "outputId": "758e329f-da58-409a-9f1f-713122fb426c"
+      },
+      "execution_count": 21,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            " 1. Dimension of the Matrix is:  3\n",
+            "\n",
+            " 3. Size of the Matrix are:  16\n",
+            "\n",
+            " 2. Total Axes of the Matrix are:  (1, 4, 4)\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#chekcing the functions like data, itemsize, dtype.\n",
+        "import numpy as np;\n",
+        "checking=np.array([[1,2,3,4], [4,5,6,7], [6,7,8,9], [3,44,56,100]]);\n",
+        "\n",
+        "#Performing the Operations in the Matrix.\n",
+        "print(\"\\n 1. Size of the Matrix: \",checking.shape);\n",
+        "print(\"\\n 2. Item-Size in the matrix: \",checking.itemsize);\n",
+        "print(\"\\n 3. Checking the Data-type of item presented in Matrix are: \",checking.dtype);\n",
+        "print(\"\\n 4. Checking the Data-type of item presented in Matrix are: \",checking.dtype.itemsize);"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 0
+        },
+        "id": "pbSktuXi1sRe",
+        "outputId": "8f18ff3e-c8a6-4b74-a048-6433ad7bcd67"
+      },
+      "execution_count": 22,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            " 1. Size of the Matrix:  (4, 4)\n",
+            "\n",
+            " 2. Item-Size in the matrix:  8\n",
+            "\n",
+            " 3. Checking the Data-type of item presented in Matrix are:  int64\n",
+            "\n",
+            " 4. Checking the Data-type of item presented in Matrix are:  8\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#Arange() functions simply gives you the initial-ending point values within the Ranges.\n",
+        "import numpy as np;\n",
+        "a=np.arange(10);\n",
+        "print(\"\\n Values: \",a);"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 0
+        },
+        "id": "Kcmb7ogF2lxf",
+        "outputId": "372880c8-6cf9-4a12-ba1e-7cf2ab10544b"
+      },
+      "execution_count": 24,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            " Values:  [0 1 2 3 4 5 6 7 8 9]\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#Some Functions based on Arange() Functions.\n",
+        "import numpy as np;\n",
+        "aa=np.arange(1,10,2);   #(START | END | STEP-SIZE)!\n",
+        "print(\"\\n The Values of aa: \",aa);\n",
+        "\n",
+        "#We, will also using floating points values within the Arange Functions.\n",
+        "b=np.arange(1,10,0.5);   #(START | END | STEP-SIZE)!\n",
+        "print(\"\\n Hence, Values of b: \",b);\n",
+        "\n",
+        "#Total Size of the Array will be Calculated!\n",
+        "c=np.arange(1,10,0.2);   #(START | END | STEP-SIZE)!\n",
+        "print(\"\\n Values of c: \",c,\" and size is: \",c.size,\" Similar in terms with the shape(): \",c.shape);"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 0
+        },
+        "id": "IIH2QV1a27W7",
+        "outputId": "7ffa1015-4246-4c98-9c6c-5b5d7b623d38"
+      },
+      "execution_count": 27,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            " The Values of aa:  [1 3 5 7 9]\n",
+            "\n",
+            " Hence, Values of b:  [1.  1.5 2.  2.5 3.  3.5 4.  4.5 5.  5.5 6.  6.5 7.  7.5 8.  8.5 9.  9.5]\n",
+            "\n",
+            " Values of c:  [1.  1.2 1.4 1.6 1.8 2.  2.2 2.4 2.6 2.8 3.  3.2 3.4 3.6 3.8 4.  4.2 4.4\n",
+            " 4.6 4.8 5.  5.2 5.4 5.6 5.8 6.  6.2 6.4 6.6 6.8 7.  7.2 7.4 7.6 7.8 8.\n",
+            " 8.2 8.4 8.6 8.8 9.  9.2 9.4 9.6 9.8]  and size is:  45  Similar in terms with the shape():  (45,)\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#The type of the array can also be explicitly specified at creation time:\n",
+        "import numpy as np;\n",
+        "d=np.array([11,24.4,89.7,6.35], dtype=\"complex\");\n",
+        "print(\"\\n Values of the d: \",d);\n",
+        "c=np.array([45,56.9,8.1,23,34,5], dtype=\"float\");\n",
+        "print(\"\\n Vaues of c: \",c);\n",
+        "e=np.array([45.1,56.9,8.1,2.93,3.4,5.567], dtype=\"int\");\n",
+        "print(\"\\n Vaues of e: \",e);"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 0
+        },
+        "id": "THWf159g4OSC",
+        "outputId": "4a54b161-eec0-4368-df36-4da6b924ecd3"
+      },
+      "execution_count": 31,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            " Values of the d:  [11.  +0.j 24.4 +0.j 89.7 +0.j  6.35+0.j]\n",
+            "\n",
+            " Vaues of c:  [45.  56.9  8.1 23.  34.   5. ]\n",
+            "\n",
+            " Vaues of e:  [45 56  8  2  3  5]\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#Putting all Zeros and ones in numpy array.\n",
+        "import numpy as np;\n",
+        "a=np.zeros(10);\n",
+        "print(\"\\n Values are: \",a);\n",
+        "c=np.zeros((3,4));\n",
+        "print(\"\\n Values of Matrix are: \",c);\n",
+        "\n",
+        "#Putting Values in form of Ones.\n",
+        "b=np.ones((2,3,4));\n",
+        "print(\"\\n Matrix: \",b);\n",
+        "e=np.ones((2,3), dtype=\"complex\");\n",
+        "print(\"\\n Complex-Matrix: \",e);"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 0
+        },
+        "id": "T7busgFM5Omd",
+        "outputId": "e9459ff3-c5e7-48d2-cf92-7935073dadb8"
+      },
+      "execution_count": 38,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            " Values are:  [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
+            "\n",
+            " Values of Matrix are:  [[0. 0. 0. 0.]\n",
+            " [0. 0. 0. 0.]\n",
+            " [0. 0. 0. 0.]]\n",
+            "\n",
+            " Matrix:  [[[1. 1. 1. 1.]\n",
+            "  [1. 1. 1. 1.]\n",
+            "  [1. 1. 1. 1.]]\n",
+            "\n",
+            " [[1. 1. 1. 1.]\n",
+            "  [1. 1. 1. 1.]\n",
+            "  [1. 1. 1. 1.]]]\n",
+            "\n",
+            " Complex-Matrix:  [[1.+0.j 1.+0.j 1.+0.j]\n",
+            " [1.+0.j 1.+0.j 1.+0.j]]\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#Conversion of lst in the form of an Array!\n",
+        "import numpy as np;\n",
+        "p=np.array([1,23,4,5]);\n",
+        "q=[67,89,90,12,2,34];\n",
+        "#Convert(list-form into an array!);\n",
+        "r=np.array(q);\n",
+        "print(\"\\n Values of r: \",r);"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 0
+        },
+        "id": "Vt5Cv3GF66QM",
+        "outputId": "2a15353c-c7bd-48b9-cc13-edcdc4bd8ac6"
+      },
+      "execution_count": 39,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            " Values of r:  [67 89 90 12  2 34]\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#Arange Functions is used to give data values within the range but not-precise & Accurate takes mre time in calculation for complex data-sets.\n",
+        "#The Linespace() function is used in place of arange() values.\n",
+        "# linespace() gives correct & accurate results data-sets.\n",
+        "import numpy as np;\n",
+        "a=np.linspace(1,10,9);\n",
+        "print(\"\\n Values of a: \",a);\n",
+        "print(\"\\n Total Size of data from linespace are: \",a.size);"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 0
+        },
+        "id": "K8jxPJxR7sgF",
+        "outputId": "147ff63a-c975-4414-d6f0-ce7ea235a64a"
+      },
+      "execution_count": 42,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            " Values of a:  [ 1.     2.125  3.25   4.375  5.5    6.625  7.75   8.875 10.   ]\n",
+            "\n",
+            " Total Size of data from linespace are:  9\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#Printing of an Arrays.\n",
+        "a=np.arange(15);\n",
+        "print(\"\\n 1. Arrange Values: \",a);\n",
+        "\n",
+        "b=np.arange(1,10,0.5); #(START | END | STEP-SIZE);\n",
+        "print(\"\\n 2. Values of b: \",b);\n",
+        "\n",
+        "c=np.array([[12,23,34,54],[34,44,56,67]], dtype=\"complex\");\n",
+        "print(\"\\n 3. Array of c: \",c);\n",
+        "\n",
+        "d=np.arange(12).reshape(3,4);\n",
+        "print(\"\\n 4. Reshaping of d: \",d);\n",
+        "\n",
+        "e=np.arange(24).reshape(2,3,4);\n",
+        "print(\"\\n 5. Reshaping values of e: \",e);"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 0
+        },
+        "id": "hcrg-f_2_FhB",
+        "outputId": "e3a338c2-dfb6-4730-f075-7f5a08e5d9b1"
+      },
+      "execution_count": 44,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            " 1. Arrange Values:  [ 0  1  2  3  4  5  6  7  8  9 10 11 12 13 14]\n",
+            "\n",
+            " 2. Values of b:  [1.  1.5 2.  2.5 3.  3.5 4.  4.5 5.  5.5 6.  6.5 7.  7.5 8.  8.5 9.  9.5]\n",
+            "\n",
+            " 3. Array of c:  [[12.+0.j 23.+0.j 34.+0.j 54.+0.j]\n",
+            " [34.+0.j 44.+0.j 56.+0.j 67.+0.j]]\n",
+            "\n",
+            " 4. Reshaping of d:  [[ 0  1  2  3]\n",
+            " [ 4  5  6  7]\n",
+            " [ 8  9 10 11]]\n",
+            "\n",
+            " 5. Reshaping values of e:  [[[ 0  1  2  3]\n",
+            "  [ 4  5  6  7]\n",
+            "  [ 8  9 10 11]]\n",
+            "\n",
+            " [[12 13 14 15]\n",
+            "  [16 17 18 19]\n",
+            "  [20 21 22 23]]]\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#NOTE: If, the Values too much greater then numpy automatically skips the central data and show only corners-Values.\n",
+        "a=np.arange(10000);\n",
+        "print(\"\\n Values of a: \",a);   #Larger Values it skip central data without showing corner-data has been visible.\n",
+        "\n",
+        "b=np.arange(100).reshape(10,10);\n",
+        "print(\"\\n Values of b: \",b);   #All Value visible without skip because data-set avilable in smaller-size.\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 0
+        },
+        "id": "URoYq4sZCeoF",
+        "outputId": "e3481a21-5d2d-4066-e7d3-cf4d8950a2a8"
+      },
+      "execution_count": 46,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            " Values of a:  [   0    1    2 ... 9997 9998 9999]\n",
+            "\n",
+            " Values of b:  [[ 0  1  2  3  4  5  6  7  8  9]\n",
+            " [10 11 12 13 14 15 16 17 18 19]\n",
+            " [20 21 22 23 24 25 26 27 28 29]\n",
+            " [30 31 32 33 34 35 36 37 38 39]\n",
+            " [40 41 42 43 44 45 46 47 48 49]\n",
+            " [50 51 52 53 54 55 56 57 58 59]\n",
+            " [60 61 62 63 64 65 66 67 68 69]\n",
+            " [70 71 72 73 74 75 76 77 78 79]\n",
+            " [80 81 82 83 84 85 86 87 88 89]\n",
+            " [90 91 92 93 94 95 96 97 98 99]]\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#Basic Operations In Numpy.\n",
+        "#Random Values Generations.\n",
+        "import numpy as np\n",
+        "a=np.random.random((2,3));\n",
+        "print(\"\\n Values of a: \",a);\n",
+        "\n",
+        "#After, every execution it can generates random values.\n",
+        "\n",
+        "#We, will talk about 'seed()' part of the random functions.\n",
+        "b=np.random.seed((1,50,2));\n",
+        "print(\"\\n Values of b: \",b);\n",
+        "\n",
+        "#generating random values and then finding the values like: max() & min().\n",
+        "import numpy as np;\n",
+        "e=np.random.random((2,3));\n",
+        "print(\"\\n\",e);\n",
+        "print(\"\\n Maximum Values: \",e.max());\n",
+        "print(\"\\n Minimum Values: \",e.min());\n",
+        "print(\"\\n Total Sum is: \",e.sum());"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 0
+        },
+        "id": "JIWuHCzEDMtC",
+        "outputId": "8dafed38-bd0a-4d07-de62-8e786a5ebd64"
+      },
+      "execution_count": 65,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            " Values of a:  [[0.59221332 0.50408614 0.22183998]\n",
+            " [0.38476062 0.13912162 0.9979631 ]]\n",
+            "\n",
+            " Values of b:  None\n",
+            "\n",
+            " [[0.45666348 0.35765781 0.80253672]\n",
+            " [0.38600085 0.97943691 0.16054335]]\n",
+            "\n",
+            " Maximum Values:  0.9794369113257708\n",
+            "\n",
+            " Minimum Values:  0.16054335039498624\n",
+            "\n",
+            " Total Sum is:  3.14283912438308\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#Random Integer Number Generations.\n",
+        "import numpy as np;\n",
+        "no1=np.random.randint(10,50,size=(50));\n",
+        "print(\"\\n Values of no1: \",no1);\n",
+        "print(\"\\n Total Size of an Arrany: \",no1.size);\n",
+        "\n",
+        "#Generating Random Integer Numbers."
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 0
+        },
+        "id": "VaGOLKDVIExD",
+        "outputId": "263bbf3d-456a-47a7-e63b-113bf13fb519"
+      },
+      "execution_count": 71,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            " Values of no1:  [30 11 36 44 37 25 14 16 28 37 48 15 14 30 39 24 44 11 33 34 34 12 18 26\n",
+            " 44 28 39 23 48 45 24 48 46 41 25 23 26 15 22 14 28 18 41 16 14 13 14 26\n",
+            " 49 49]\n",
+            "\n",
+            " Total Size of an Arrany:  50\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# few more numpy functions\n",
+        "\n",
+        "d = np.zeros((10))\n",
+        "print(d)\n",
+        "r = np.ones((10))*2\n",
+        "print(r)\n",
+        "\n",
+        "g = np.full((10),100)\n",
+        "print(g)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 0
+        },
+        "id": "RlX1-lRoIp3_",
+        "outputId": "9a9f7a93-406e-4bf7-a185-b70e779db812"
+      },
+      "execution_count": 72,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "[0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
+            "[2. 2. 2. 2. 2. 2. 2. 2. 2. 2.]\n",
+            "[100 100 100 100 100 100 100 100 100 100]\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# statistical function\n",
+        "np.random.seed(10)\n",
+        "var2 = np.random.randint(10,100,(50))\n",
+        "\n",
+        "s = var2.sum()\n",
+        "t = var2.min()\n",
+        "m = var2.max()\n",
+        "max_ind = var2.argmax()\n",
+        "min_ind = var2.argmin()\n",
+        "avg = var2.mean()\n",
+        "print(var2)\n",
+        "print()\n",
+        "print(\"min: \",t,'Pos:',min_ind)\n",
+        "print(\"max: \",m,'Pos:',max_ind)\n",
+        "print(\"average: \",avg)\n",
+        "print(\"sum of all the elements: \",s )\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 0
+        },
+        "id": "gK1mNAHeJgyM",
+        "outputId": "53bf4629-1eba-43ad-dc5a-5adbad39d56f"
+      },
+      "execution_count": 73,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "[19 25 74 38 99 39 18 83 10 50 46 26 21 64 98 72 43 82 88 59 61 64 87 79\n",
+            " 23 35 23 96 40 40 99 22 75 41 67 46 37 28 87 32 33 21 38 84 98 19 25 28\n",
+            " 90 81]\n",
+            "\n",
+            "min:  10 Pos: 8\n",
+            "max:  99 Pos: 4\n",
+            "average:  53.06\n",
+            "sum of all the elements:  2653\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#Userlibraries and custom libraries as well!\n",
+        "import matplotlib.pyplot as plt;\n",
+        "import numpy as np;\n",
+        "\n",
+        "X=np.linspace(1,20,100);\n",
+        "Y=np.sin(X);\n",
+        "Z=np.cos(X);\n",
+        "W=2*np.sin(X)+np.cos(X);\n",
+        "P=np.sin(2*X)-2*np.cos(X);\n",
+        "plt.figure(1,figsize=(5,2));\n",
+        "plt.plot(X,Y,'r',linewidth='4');\n",
+        "plt.title(\"Clock-Pendulum Motion!\");\n",
+        "plt.xlabel(\"Sin-Functions\");\n",
+        "plt.ylabel(\"Cos-Functions\");\n",
+        "plt.grid(\"on\");\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 256
+        },
+        "id": "toT4_BFrP5f-",
+        "outputId": "1f2aa27f-286d-4601-9786-fb0654811049"
+      },
+      "execution_count": 79,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 500x200 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import matplotlib.pyplot as plt;\n",
+        "import numpy as np;\n",
+        "\n",
+        "#We, takes the Values of the Data-sets Conditions.\n",
+        "X=np.linspace(1,20,100);\n",
+        "Y=np.sin(X);\n",
+        "Z=np.cos(X);\n",
+        "W=2*np.sin(X)+np.cos(X);\n",
+        "P=np.sin(2*X)-2*np.cos(X);\n",
+        "plt.figure(1,figsize=(5,2));\n",
+        "plt.plot(X,Y,'m',linewidth='4');\n",
+        "plt.title(\"Moving Speed of the Car (Graphical - Form)\");\n",
+        "plt.xlabel(\"Time\");\n",
+        "plt.ylabel(\"Speed\");\n",
+        "plt.grid(\"on\");\n",
+        "\n",
+        "#Another Graphical Manner!\n",
+        "plt.figure(1,figsize=(5,2));\n",
+        "plt.plot(X,Z,'c',linewidth='4');\n",
+        "plt.title(\"Moving Speed of the Car (Graphical - Form)\");\n",
+        "plt.xlabel(\"Time\");\n",
+        "plt.ylabel(\"Speed\");\n",
+        "plt.grid(\"on\");\n",
+        "\n",
+        "#3rd Graphical Forms!\n",
+        "plt.figure(1,figsize=(5,2));\n",
+        "plt.plot(X,P,'r',linewidth='4');\n",
+        "plt.title(\"Moving Speed of the Car (Graphical - Form)\");\n",
+        "plt.xlabel(\"Time\");\n",
+        "plt.ylabel(\"Speed\");\n",
+        "plt.grid(\"on\");\n",
+        "\n",
+        "#4th Graphical Forms!\n",
+        "plt.figure(1,figsize=(5,2));\n",
+        "plt.plot(X,W,'b',linewidth='4');\n",
+        "plt.title(\"Moving Speed of the Car (Graphical - Form)\");\n",
+        "plt.xlabel(\"Time\");\n",
+        "plt.ylabel(\"Speed\");\n",
+        "plt.grid(\"on\");\n",
+        "\n",
+        "#5th Graphical Forms!\n",
+        "plt.figure(1,figsize=(5,2));\n",
+        "plt.plot(X,Y,'d',linewidth='4');\n",
+        "plt.title(\"Moving Speed of the Car (Graphical - Form)\");\n",
+        "plt.xlabel(\"Time\");\n",
+        "plt.ylabel(\"Speed\");\n",
+        "plt.legend();\n",
+        "plt.grid(\"on\");\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 294
+        },
+        "id": "tpLxMeUFRmFp",
+        "outputId": "1d587b05-5e13-4e65-90e0-cf2084130f2c"
+      },
+      "execution_count": 94,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "WARNING:matplotlib.legend:No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 500x200 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#Generate a Graph with (Acceleration - Velocity Time Graph!).\n",
+        "import matplotlib.pyplot as plt;\n",
+        "import numpy as np;\n",
+        "\n",
+        "#Putting & Generating the Values of the Data.\n",
+        "X=np.linspace(1,50,100);\n",
+        "Y=np.sin(X);\n",
+        "Z=np.cos(X);\n",
+        "W=2*np.sin(X)+np.cos(X);\n",
+        "P=np.sin(2*X)-2*np.cos(X);\n",
+        "\n",
+        "#Designing the Figure suitable for the Human-Interface!\n",
+        "plt.figure(1,figsize=(10,4),label=\"Sin(X)\");\n",
+        "plt.plot(X,Y,\"r\",linewidth='4',label=\"Sin(X)\");\n",
+        "plt.plot(X,Z,\"b\",linewidth='4',label=\"Cos(X)\");\n",
+        "plt.plot(X,W,\"g\",linewidth='4',label=\"Func1(X)\");\n",
+        "plt.plot(X,P,\"m\",linewidth='4',label=\"Func2(X)\");\n",
+        "\n",
+        "#Finalizing & Designing , Mentioning the Details related to the Graph!\n",
+        "plt.title(\"Velocity/Acceleraation (Time-Graph)\");\n",
+        "plt.xlabel(\"Velocity\");\n",
+        "plt.ylabel(\"Acceleration\");\n",
+        "plt.legend();\n",
+        "plt.grid(\"on\");"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 410
+        },
+        "id": "n9DY80THT8pa",
+        "outputId": "b1c2c3f0-7f50-4618-8ad2-d0e99054698f"
+      },
+      "execution_count": 103,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x400 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    }
+  ]
+}
\ No newline at end of file
diff --git a/Day1_dhruvdhayal_ai.py b/Day1_dhruvdhayal_ai.py
new file mode 100644
index 0000000..2b0923c
--- /dev/null
+++ b/Day1_dhruvdhayal_ai.py
@@ -0,0 +1,403 @@
+# -*- coding: utf-8 -*-
+"""Day1_DhruvDhayal_AI.ipynb
+
+Automatically generated by Colab.
+
+Original file is located at
+    https://colab.research.google.com/drive/1RsqcV02nwFqsVaetoZpiAJNGgpxcE3Mm
+"""
+
+#Day-1 Of_AI_ML_SUMMER_TRAINING.
+print("\n --------------- Factorial Program! ---------------");
+n=int(input("\n 1. Enter the Number in order to find it's Factorial: "));
+fact=1;
+for i in range(1,n+1):
+  i=i+1;
+  fact=fact*i;
+print("\n --> Factorial of ",n," is: ",fact);
+
+#Sum of the 'n' Numbers.
+print("\n -------------- Sum of the Two or More Numbers! ------------------");
+n=int(input("\n 1. Enter the Number in order to find it's Sum: "));
+sum=0;
+for i in range(1,n+1):
+  sum=sum+i;
+  i=i+1;
+  print("\t", sum);
+print("\n --> Sum of ",n," is: ",sum);
+
+#Adding of the Two Numbers.
+#1.  Manual Methods.
+a=10;
+b=90;
+c=(a+b);
+print("\n Total Sum of ",a," and ",b," is: ",c);
+
+#2. User-Input Methods.
+a=int(input("\n 1. Enter the First Number: "));
+b=int(input("\n 2. Enter the Second Number: "));
+result=(a+b);
+print(f"\n\t--> Total Sum of {a} and {b} is: {result}");
+
+#Using of the While Loop, or never-ending loop.
+while(True):
+  print("Hello World");  #We, did not print this because it takes lot of memory and space, which may lead to bann of GoogleVColabLisecing.
+
+#WAP a program to perform Arithmetic Operations (+,-,*,/), on the two no's entered by the user and perform operations on ot!
+#(Operations has to been perfored)!
+#Then, Ask the User a Question: Do, You Want to Continue?
+#If 'n' Exists then its still performing the operations based on the True Valid-Conditions.
+flag=True;
+while(flag):
+  n1=int(input("\n 1. Enter the First Number: "));
+  n2=int(input("\n 2. Enter the Second Number: "));
+  ch=input("\n --> Enter the Operations You want to Perform: \n 1. Addition(+) \n2.Substraction(-) \n3.Multiplication(*) \n4.Division(/) \n\n");
+
+  #Checking the Operation in order to performs.
+  if(ch=='+'):
+    n3=(n1+n2);
+    print("\n\n --> Addition of (",n1,"+",n2,") is = ",n3);
+  elif(ch=='-'):
+    n3=(n1-n2);
+    print("\n --> Substraction of (",n1,"-",n2,") is = ",n3);
+  elif(ch==''):
+     n3=(n1*n2);
+     print("\n --> Multiplication of (",n1,"*",n2,") is = ",n3);
+  elif(ch=='/'):
+    if(n1>n2):
+      n3=(n1/n2);
+      print("\n --> Division of (",n1,"/",n2,") is = ",n3);
+    else:
+      n3=(n2/n1);
+      print("\n --> Division of (",n2,"/",n1,") is = ",n3);
+  else:
+    print("\n Invalid Input!");
+
+
+#Main Driver Source Code Logic Of User-Input
+#print("\n ----- Do, You Want to Still continiue Choose (Y/N): ");
+  user=input("\n----- Do, You Want to Still continiue Choose (Y/N): ");
+  flag=0;
+  if(user=="Y" or user=="y"):
+    flag=True;
+    print("\n Now, It's Again Executed!");
+    print("\n---------------------------------------\n");
+  else:
+    flag=False;
+    print("\n Program Terminated Thank You!");
+    print("\n---------------------------------------\n");
+
+#Performing the Same Logic Program with the Functions.
+#Starting Phase!
+
+def values():
+  no1=int(input("\n 1. Enter the First Number: "));
+  no2=int(input("\n 2. Enter the Second Number: "));
+  ch=input("\n --> Enter the Operations You want to Perform: \n 1. Addition(+) \n2.Substraction(-) \n3.Multiplication(*) \n4.Division(/) \n\n");
+  return no1,no2,ch;
+
+def operations(no1,no2,ch):
+  #Middle-Phase Performing Operations.
+  if(ch=='+'):
+    n3=(no1+no2);
+    print("\n\n --> Addition of (",no1,"+",no2,") is = ",n3);
+  elif(ch=='-'):
+    n3=(no1-no2);
+    print("\n --> Substraction of (",no1,"-",no2,") is = ",n3);
+  elif(ch=='*'):
+    n3=(no1*no2);
+    print("\n --> Multiplication of (",no1,"*",no2,") is = ",n3);
+  elif(ch=='/'):
+    if(no1>no2):
+      n3=(no1/no2);
+      print("\n --> Division of (",no1,"/",no2,") is = ",n3);
+    else:
+      n3=(no2/no1);
+      print("\n --> Division of (",no2,"/",no1,") is = ",n3);
+  else:
+    print("\n Invalid Input!");
+
+def checking():
+  #Main-Driver Source Code.
+  user=input("\n --> Do, You Still Want to Continue? (Y/N): ");
+  if(user=="Y" or user=="y"):
+    return True;
+    print("\n Now, It's Again Executed!");
+    print("\n---------------------------------------\n");
+  else:
+    return False;
+    print("\n Program Terminated!");
+    print("\n---------------------------------------\n");
+
+#Main Calling the Values of the Functions!
+flag=True;
+while(flag):
+  no1,no2,ch=values();
+  operations(no1,no2,ch);
+  flag=checking();
+
+#Import third party Library in Python Instance.
+#Command to install third_party_Library.
+#Pip Install Name of the Three Modules.
+
+!pip install numpy;
+
+"""<!  Starting of the Numpy Library and its's Functions & Operations.>"""
+
+#Now, Importing the Numpy & then perform all functions and it's operatiosns.
+
+import numpy as np
+V=np.array([1,2,3,4,5]);
+D=[1,2,3,4,5];
+print("\n Values of ",V," and type of item it Contains: ",type(V));
+print("\n Values of ",D," and type of item it Contains: ",type(D));
+
+#checking the Dimensions of the Array.
+#NOTE: In, Python Numpy we used to call Dimensions as an axes!
+import numpy as np;
+check=np.array([[1,2] , [3,4] , [5,6],  [7,8]]);
+print("\n Dimensions of the Array: ",check.ndim);
+
+#Calculating the Total Size of an Array!
+#NOTE: Both, shape() & size() is similar to find the total length of item in any dimension of numpy array!
+import numpy as np;
+checking=np.array([[[1,2,3,4], [4,5,6,7], [6,7,8,9], [3,44,56,100]]]);
+
+#Checking the Dimensions of the Matrix.
+print("\n 1. Dimension of the Matrix is: ",checking.ndim);
+print("\n 3. Size of the Matrix are: ",checking.size);
+print("\n 2. Total Axes of the Matrix are: ",checking.shape);
+
+#chekcing the functions like data, itemsize, dtype.
+import numpy as np;
+checking=np.array([[1,2,3,4], [4,5,6,7], [6,7,8,9], [3,44,56,100]]);
+
+#Performing the Operations in the Matrix.
+print("\n 1. Size of the Matrix: ",checking.shape);
+print("\n 2. Item-Size in the matrix: ",checking.itemsize);
+print("\n 3. Checking the Data-type of item presented in Matrix are: ",checking.dtype);
+print("\n 4. Checking the Data-type of item presented in Matrix are: ",checking.dtype.itemsize);
+
+#Arange() functions simply gives you the initial-ending point values within the Ranges.
+import numpy as np;
+a=np.arange(10);
+print("\n Values: ",a);
+
+#Some Functions based on Arange() Functions.
+import numpy as np;
+aa=np.arange(1,10,2);   #(START | END | STEP-SIZE)!
+print("\n The Values of aa: ",aa);
+
+#We, will also using floating points values within the Arange Functions.
+b=np.arange(1,10,0.5);   #(START | END | STEP-SIZE)!
+print("\n Hence, Values of b: ",b);
+
+#Total Size of the Array will be Calculated!
+c=np.arange(1,10,0.2);   #(START | END | STEP-SIZE)!
+print("\n Values of c: ",c," and size is: ",c.size," Similar in terms with the shape(): ",c.shape);
+
+#The type of the array can also be explicitly specified at creation time:
+import numpy as np;
+d=np.array([11,24.4,89.7,6.35], dtype="complex");
+print("\n Values of the d: ",d);
+c=np.array([45,56.9,8.1,23,34,5], dtype="float");
+print("\n Vaues of c: ",c);
+e=np.array([45.1,56.9,8.1,2.93,3.4,5.567], dtype="int");
+print("\n Vaues of e: ",e);
+
+#Putting all Zeros and ones in numpy array.
+import numpy as np;
+a=np.zeros(10);
+print("\n Values are: ",a);
+c=np.zeros((3,4));
+print("\n Values of Matrix are: ",c);
+
+#Putting Values in form of Ones.
+b=np.ones((2,3,4));
+print("\n Matrix: ",b);
+e=np.ones((2,3), dtype="complex");
+print("\n Complex-Matrix: ",e);
+
+#Conversion of lst in the form of an Array!
+import numpy as np;
+p=np.array([1,23,4,5]);
+q=[67,89,90,12,2,34];
+#Convert(list-form into an array!);
+r=np.array(q);
+print("\n Values of r: ",r);
+
+#Arange Functions is used to give data values within the range but not-precise & Accurate takes mre time in calculation for complex data-sets.
+#The Linespace() function is used in place of arange() values.
+# linespace() gives correct & accurate results data-sets.
+import numpy as np;
+a=np.linspace(1,10,9);
+print("\n Values of a: ",a);
+print("\n Total Size of data from linespace are: ",a.size);
+
+#Printing of an Arrays.
+a=np.arange(15);
+print("\n 1. Arrange Values: ",a);
+
+b=np.arange(1,10,0.5); #(START | END | STEP-SIZE);
+print("\n 2. Values of b: ",b);
+
+c=np.array([[12,23,34,54],[34,44,56,67]], dtype="complex");
+print("\n 3. Array of c: ",c);
+
+d=np.arange(12).reshape(3,4);
+print("\n 4. Reshaping of d: ",d);
+
+e=np.arange(24).reshape(2,3,4);
+print("\n 5. Reshaping values of e: ",e);
+
+#NOTE: If, the Values too much greater then numpy automatically skips the central data and show only corners-Values.
+a=np.arange(10000);
+print("\n Values of a: ",a);   #Larger Values it skip central data without showing corner-data has been visible.
+
+b=np.arange(100).reshape(10,10);
+print("\n Values of b: ",b);   #All Value visible without skip because data-set avilable in smaller-size.
+
+#Basic Operations In Numpy.
+#Random Values Generations.
+import numpy as np
+a=np.random.random((2,3));
+print("\n Values of a: ",a);
+
+#After, every execution it can generates random values.
+
+#We, will talk about 'seed()' part of the random functions.
+b=np.random.seed((1,50,2));
+print("\n Values of b: ",b);
+
+#generating random values and then finding the values like: max() & min().
+import numpy as np;
+e=np.random.random((2,3));
+print("\n",e);
+print("\n Maximum Values: ",e.max());
+print("\n Minimum Values: ",e.min());
+print("\n Total Sum is: ",e.sum());
+
+#Random Integer Number Generations.
+import numpy as np;
+no1=np.random.randint(10,50,size=(50));
+print("\n Values of no1: ",no1);
+print("\n Total Size of an Arrany: ",no1.size);
+
+#Generating Random Integer Numbers.
+
+# few more numpy functions
+
+d = np.zeros((10))
+print(d)
+r = np.ones((10))*2
+print(r)
+
+g = np.full((10),100)
+print(g)
+
+# statistical function
+np.random.seed(10)
+var2 = np.random.randint(10,100,(50))
+
+s = var2.sum()
+t = var2.min()
+m = var2.max()
+max_ind = var2.argmax()
+min_ind = var2.argmin()
+avg = var2.mean()
+print(var2)
+print()
+print("min: ",t,'Pos:',min_ind)
+print("max: ",m,'Pos:',max_ind)
+print("average: ",avg)
+print("sum of all the elements: ",s )
+
+#Userlibraries and custom libraries as well!
+import matplotlib.pyplot as plt;
+import numpy as np;
+
+X=np.linspace(1,20,100);
+Y=np.sin(X);
+Z=np.cos(X);
+W=2*np.sin(X)+np.cos(X);
+P=np.sin(2*X)-2*np.cos(X);
+plt.figure(1,figsize=(5,2));
+plt.plot(X,Y,'r',linewidth='4');
+plt.title("Clock-Pendulum Motion!");
+plt.xlabel("Sin-Functions");
+plt.ylabel("Cos-Functions");
+plt.grid("on");
+
+import matplotlib.pyplot as plt;
+import numpy as np;
+
+#We, takes the Values of the Data-sets Conditions.
+X=np.linspace(1,20,100);
+Y=np.sin(X);
+Z=np.cos(X);
+W=2*np.sin(X)+np.cos(X);
+P=np.sin(2*X)-2*np.cos(X);
+plt.figure(1,figsize=(5,2));
+plt.plot(X,Y,'m',linewidth='4');
+plt.title("Moving Speed of the Car (Graphical - Form)");
+plt.xlabel("Time");
+plt.ylabel("Speed");
+plt.grid("on");
+
+#Another Graphical Manner!
+plt.figure(1,figsize=(5,2));
+plt.plot(X,Z,'c',linewidth='4');
+plt.title("Moving Speed of the Car (Graphical - Form)");
+plt.xlabel("Time");
+plt.ylabel("Speed");
+plt.grid("on");
+
+#3rd Graphical Forms!
+plt.figure(1,figsize=(5,2));
+plt.plot(X,P,'r',linewidth='4');
+plt.title("Moving Speed of the Car (Graphical - Form)");
+plt.xlabel("Time");
+plt.ylabel("Speed");
+plt.grid("on");
+
+#4th Graphical Forms!
+plt.figure(1,figsize=(5,2));
+plt.plot(X,W,'b',linewidth='4');
+plt.title("Moving Speed of the Car (Graphical - Form)");
+plt.xlabel("Time");
+plt.ylabel("Speed");
+plt.grid("on");
+
+#5th Graphical Forms!
+plt.figure(1,figsize=(5,2));
+plt.plot(X,Y,'d',linewidth='4');
+plt.title("Moving Speed of the Car (Graphical - Form)");
+plt.xlabel("Time");
+plt.ylabel("Speed");
+plt.legend();
+plt.grid("on");
+
+#Generate a Graph with (Acceleration - Velocity Time Graph!).
+import matplotlib.pyplot as plt;
+import numpy as np;
+
+#Putting & Generating the Values of the Data.
+X=np.linspace(1,50,100);
+Y=np.sin(X);
+Z=np.cos(X);
+W=2*np.sin(X)+np.cos(X);
+P=np.sin(2*X)-2*np.cos(X);
+
+#Designing the Figure suitable for the Human-Interface!
+plt.figure(1,figsize=(10,4),label="Sin(X)");
+plt.plot(X,Y,"r",linewidth='4',label="Sin(X)");
+plt.plot(X,Z,"b",linewidth='4',label="Cos(X)");
+plt.plot(X,W,"g",linewidth='4',label="Func1(X)");
+plt.plot(X,P,"m",linewidth='4',label="Func2(X)");
+
+#Finalizing & Designing , Mentioning the Details related to the Graph!
+plt.title("Velocity/Acceleraation (Time-Graph)");
+plt.xlabel("Velocity");
+plt.ylabel("Acceleration");
+plt.legend();
+plt.grid("on");
\ No newline at end of file